With the popularity of information and communication technology, there are increasing demands for indoor localization. Accurate location information is needed in public places such as shopping malls, airports, exhibition halls, office buildings, warehouses, underground parking, etc. Indoor localization can be used in guiding mall shopping, large warehouse management, public places tracing, etc. Accurate location information can be used to navigate police officers, firefighters, soldiers, and medical staff in specific places and help them fulfill specific indoor tasks. However, conventional localization systems cannot meet the demands of indoor location based services. The GPS works well outdoors, but in indoor environments, the walls and concrete obstacles can block the GPS signal, which makes localization very difficult.
Current indoor localization technologies mainly include proximity detection method, fingerprint matching method, multilateration/angulation method, etc. The proximity detection method takes the location of a detected signal source as the localization position. The disadvantage of this method is its low accuracy. The fingerprint matching method produces higher accuracy according to mapping of signal characteristics with a fingerprint database. But the localization result of this method can be affected by indoor multipath effects and environmental changes; moreover it is tedious to establish the fingerprint database. The multilateration/angulation method first measures the distance/angle between a device and a reference node by hardware equipments, and then locates the device from the geometric constraints defined by the distances/angles. Many practical devices do not possess such ranging or angle measuring capability, so the localization results are inaccurate.